The Limitations of AI
But what is “human-like intelligence”? It seems to me that it is not all that different from what the likes of Google search or Siri do: absorb vast amounts of associations between data items, without really being systematic about what the associations mean or selective about their quality, and apply some statistical algorithm to the associations to pick the most relevant.
There must be more to it than that; for one thing, trained humans can sort of do actual proper logic, about a billion times less well than this netbook can, and there’s a lot of effectively hand-built (i.e. specifically evolved) functionality in a some selected pattern-recognition areas. But I think the general-purpose associationist mechanism is the most important from the point of view of building artificial intelligence.
There are good reasons to suspect that human intelligence is very close to being as good as it can get.
One is that thinking about things longer doesn’t reliably produce better conclusions. That is the point of Malcolm Gladwell’s “Blink” (as far as I understand it; I take Gladwell to be the champion of what Neal Stephenson called “those American books where once you’re heard the title you don’t even need to read it”).
The next, related, reason is that human intelligence doesn’t scale out very well; having more people think about a problem doesn’t reliably give better answers than having just one do it.
The major limitation on human intelligence, particularly when it is augmented with computers as it generally is now, is how much it is wrong. Being faster or bigger doesn’t push back the major limitation unless it can make the intelligence wrong less often, and I don’t think it would.
What I’m saying is that the major cost of human intelligence is not in the scarce resources required to execute the decision-making, but the damage caused by all the bad decisions that humans make.
The major real-world expense in obtaining high-quality human decision-makers is identifying which of the massive surplus available are actually any good. Being able to supply vastly bigger numbers of AI candidates would not drive that cost down.
It goes on and gets more interesting from there. Read the whole thing.
I found this bit from the second post particularly enlightening:
All this relates to another long-standing issue in our corner of the blogosphere: education, signalling and credentialism. The argument is that the main purpose of higher education is not to improve the abilities of the students, but merely to indicate those students who can first get into and then endure the education system itself. The implication is that there is something very wrong with this. But one way of looking at it is that the major cost is not either producing or preparing intelligent people, but testing and safely integrating them into the system. The signalling in the education system is part of that integration cost.